W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Electronics ◽

10.3390/electronics10141671 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1671

Author(s):

Jibing Gong ◽

Cheng Wang ◽

Zhiyong Zhao ◽

Xinghao Zhang

Keyword(s):

Auxiliary Information ◽

Automatic Generation ◽

Information Network ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Online Study ◽

Path Graph ◽

Proposed Model ◽

Meta Path

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.

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Heterogeneous Information Network Embedding with Meta-path Based Graph Attention Networks

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30493-5_57 ◽

2019 ◽

pp. 622-634 ◽

Cited By ~ 1

Author(s):

Meng Cao ◽

Xiying Ma ◽

Ming Xu ◽

Chongjun Wang

Keyword(s):

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Attention Networks ◽

Meta Path

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W-Com2Vec: A topic-driven meta-path- based intra-community embedding for content-based heterogeneous information network

Intelligent Data Analysis ◽

10.3233/ida-194843 ◽

2020 ◽

Vol 24 (5) ◽

pp. 1207-1233

Author(s):

Phu Pham ◽

Phuc Do

Keyword(s):

Network Analysis ◽

Biological Networks ◽

Traditional Approach ◽

Empirical Studies ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Structure Preserving ◽

Meta Path

Heterogeneous information network (HIN) are becoming popular across multiple applications in forms of complex large-scaled networked data such as social networks, bibliographic networks, biological networks, etc. Recently, information network embedding (INE) has aroused tremendously interests from researchers due to its effectiveness in information network analysis and mining tasks. From recent views of INE, community is considered as the mesoscopic preserving network’s structure which can be combined with traditional approach of network’s node proximities (microscopic structure preserving) to leverage the quality of network’s representation. Most of contemporary INE models, like as: HIN2Vec, Metapath2Vec, HINE, etc. mainly concentrate on microscopic network structure preserving and ignore the mesoscopic (intra-community) structure of HIN. In this paper, we introduce a novel approach of topic-driven meta-path-based embedding, namely W-Com2Vec (Weighted intra-community to vector). Our proposed W-Com2Vec model enables to capture richer semantic of node representation by applying the meta-path-based community-aware, node proximity preserving and topic similarity evaluation at the same time during the process of network embedding. We demonstrate comprehensive empirical studies on our proposed W-Com2Vec model with several real-world HINs. Experimental results show W-Com2Vec outperforms recent state-of-the-art INE models in solving primitive network analysis and mining tasks.

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